Only 10% of IND applications result in clinically approved drugs that reach patients because of the extreme complexity and limited understanding of genetic disease mechanisms. This uncertainty during drug development can be devastating, resulting in repeated R&D failures in the lab, diminished likelihood of success in clinical trials, and/or inappropriate diagnosis and treatment of patients in the clinic.
To mitigate these issues, advances in genomics equip both Pharma and Biopharma with new capabilities that expedite their regulatory approval journey. These include:
- Choosing better starting points for therapies,
- Reducing R&D costs by focusing on validated targets,
- Increasing the chance of FDA approval with genetic evidence support,
- Increasing success of clinical trials through optimized patient selection, and
- Decreasing time to market.
In this panel discussion, precision medicine executives from Rhythm Pharmaceuticals, Alexion, and Biogen are joined by genomics expert Dr. Mark Kiel. Armed with expertise in translational research, strategic diagnostics, and medical genetics, as well as a comprehensive understanding of biomarker discovery, these thought leaders explore the deep and invaluable connection between drug development and genomics – and how they are driving innovation with genomic knowledge within Pharma.
Hello, everyone, and welcome to this Genomic Engager Event, focusing on how genetics is being used to drive drug discovery and development, an exclusive event providing leaders in pharma and biotech with an opportunity to explore the deep and invaluable connection between drug development and genomics.
To briefly introduce myself, I am Noemí, a member of the CDx Engage team here at Hanson Wade. It is my pleasure to welcome you all to this exclusive online event. I hope you’ve taken advantage of your Engager kits we’ve sent your way. Grab a hot cup of tea or a hot cup of coffee from them, and you’re all set and ready to enjoy today’s session. My colleague Annie will be sending out a form after the Engager, so if you still want to receive a kit, please fill it in and we will make sure you receive one as soon as possible.
As we discuss how you can improve your genomic strategy to establish more efficacious clinical trials, this will with no doubt raise many questions amongst you all today. That’s why during the final discussion, the floor is open to you at this exclusive event. There are many ways you can get involved with lots of platform functionalities, giving you the opportunity to discuss your pressing questions alongside your peers joining us today.
With this in mind, before I hand it over to today’s host, Genomenon, I’d like to quickly remind everyone online of how you can take advantage of the interactivity allowed on this online platform. First of all, your comments and questions are welcomed and encouraged during the panel discussion. They will be collected and addressed during the Q&A session. You can post via the Q&A tab under “session,” and I will read out the questions to the panelists at the end of the discussion. You should be able to find this tab on the right hand side of your screen. You can also upvote the questions you like by clicking on the thumbs up icon next to each question submitted. Following today’s panel discussion and Q&A, you will all be invited to take part in group networking to dig a bit deeper into other topics covered at today’s event, or to simply meet up with old and new connections.
You can also reach out to specific attendees by using the “people” tab on the right hand side. As well as messaging someone in a private chat, you can also use this function to invite people to video calls and create private one-to-one chat rooms, so you can still have those face-to-face interactions we’ve all been missing. If you haven’t already, you can personalize your profile so your colleagues know exactly who you are and who they are speaking with. To do this, just click on the top right hand corner where your initials are displayed and select “Edit Profile.” If you have any questions or any trouble navigating the platform, please reach out to my colleague, Annie, the event manager. You can search for her via the “people” tab. Also with us on the platform today from the Engage team are Sam Saguar and Dominic Allen. Please feel free to reach out to them with any questions.
Without further ado, I would like to introduce Dr. Mark Kiel, co-founder and Chief Science Officer at Genomenon. Mark oversees the company’s scientific direction and product development at Genomenon. After 15 years in academic research, Mark became convinced that revolutionary change in genomics was more likely to emerge out of industry. In 2014, he founded Genomenon, an artificial-intelligence-driven life science company addressing the challenge of connecting pharma researchers with evidence in the literature to help diagnose and treat patients with genetic disease and cancer. Welcome, Mark!
MARK: Thank you so much, Noemí. Thank you also to the attendees, welcome, everyone, and a special thank you to our panelists who’ve gotten together here to discuss today’s important topic! As Noemí mentioned, my name is Mark. I’m the Chief Science Officer and founder of Genomenon. My background is as an MD/PhD physician scientist with a specialty in molecular genetic pathology.
I was asked to provide a bit of context by way of an introduction to today’s proceedings, and in thinking about what I wanted to discuss, I was reflecting on the lived history of my scientific and clinical career as pertains to the Human Genome Project and the next-generation DNA sequencing revolution. I just wanted to touch on some of those points to put where we are in history into context as we discuss, as some of these questions that we’re going to go through relate not just to genetics and clinical practice, but also to drug development.
So I was coming up as a graduate student when the Human Genome Project was nearing completion. That dates me somewhat, but I’m not ashamed to admit it. It was a very exciting time for me as a graduate student, because I saw that there was a way to conduct science without doing animal husbandry or laborious two-year-long experiments. The Human Genome Project was the culmination of multiple countries’ efforts and many billions of dollars and more than a decade worth of work. It made the idea that unlocking the power of the genome for clinical purposes was possible. As I was evolving in my graduate career and entering into my postdoctoral work, the next-generation DNA sequencing revolution really took off, so it was at the tail end of the Human Genome Project. That was a component of accelerating that work, but it really flourished first in research, and then eventually became a routine clinical practice while I was finishing up my training. That suggested the next-generation DNA sequencing revolution — sequencing a patient’s genome — was plausible, based on those technological advances.
In that environment, I was considering where my career would take me, and as Noemí alluded to, I was frustrated by the incremental linear pace within academics. I sought to make bolder, more dramatic change through industry. Where I turned my attention, following the arc of the Human Genome Project and the next-generation sequencing revolution, was in breaking this so-called bioinformatic bottleneck, or the challenge of interpreting all of this information, whether it be for research purposes and drug discovery, or applications of a certain assay type in the clinic. That interpretation challenge was really where I saw there needing to be the most advancement to truly unlock the potential of the genome, both in the clinic and in research. Founding Genomenon so many years ago, at this point, I really sought to make clinical genome sequencing and its applications practical. The Human Genome Project made it possible; next-generation sequencing made it plausible; and here we are now, talking about how the bioinformatic bottleneck has been broken, and we’re now able to make practical the business of interpreting entire genomes and entire cohorts worth of genomes.
In my role as the Chief Science Officer of Genomenon, I chaperone our offerings into the clinic for genome sequencing and related diagnostics. That has been true since the founding of Genomenon eight years ago now. With particular relevance to what we’re going to talk about today, in the past three to four years, what we’re seeing is a great need for and usefulness of applications of these technologies to solve the interpretation challenge in pharma for drug discovery and early phases of the drug development pipeline, as well as in clinical trials and in commercialization activity. So as I suggested, I’m here mostly to learn! I’ve had a number of engaging conversations with our panelists, and so I’m going to now cede the floor back to Noemí. I’ll weigh in where I think it’s relevant if I can’t help myself, but otherwise, I’ll let our panelists run with the questions.
NOEMÍ: Thank you, Mark for this introduction! I would now like to introduce the rest of the panelists joining us today.
First, Dr. Alastair Garfield, Senior Vice President and Head of Research for Rhythm pharmaceuticals. Alastair is recognized as a leading expert on the mc4r pathway and the neurogenetics of body weight, having worked in this space for over a decade. Prior to joining Rhythm, Al was Laboratory Head at Pfizer’s Cardiovascular Metabolic Disease Research Unit, where he led target identification and validation efforts for the eating disorders group.
We also have Dr. Tom Defay, Deputy Head of Diagnostics Strategy & Development for Alexion pharmaceuticals. Tom received his PhD from the University of California in San Francisco. He started his career in bioinformatics and data science, eventually leading teams in genomics and molecular sciences. Now, Tom is Deputy Head of the Diagnostic Center of Excellence at Alexion, and is working to shorten the diagnostic odyssey for patients with rare disease.
Finally, Dr. Heiko Runz, Head of Human Genetics and Medical Director of Genetics for Biogen. Heiko is a board-certified medical geneticist and translational scientist with experience in all aspects of clinical genetics and basic training in pediatrics. The focus of his research is to generate therapeutic hypotheses from genetic data, and to translate these ideas into preclinical drug discovery programs, as well as the design of clinical trials.
Now, please, Alastair, Tom, and Heiko, join me on stage by sharing your audio and video.
TOM, HEIKO: Hello! (WAVE)
NOEMÍ: Well, thank you all for joining me! While we wait for Al, I’d just like to remind the audience that they can submit their questions via the Q&A tab throughout the whole session, and the answers will be replied by the panelists just at the end of the panel discussion.
While we wait for Al, I want to start with the panel discussion. I’d like to say that it is not really known how many patients with rare diseases are under-diagnosed, but it is commonly believed that even when a diagnostic and an effective treatment are both available for a disease, a really small percentage of patients are diagnosed and receive that treatment. What resources are available to address this issue? Heiko, maybe do you want to start?
HEIKO: Yes, happy to! Thank you so much for organizing this panel discussion today, and also for having me in this role. Your question was in reference to the rare disease space, and about what sort of resources we have to make conclusions from genetic data. Listening to how Mark introduced himself, I’d like to take a step back as well and go into my personal history. I worked as a medical geneticist, and during that training, was also running a neurogenetic clinical diagnostic lab. In many instances, that involved communicating genetic findings to patients and families in genetic counseling. I must say, what has moved me away from this trajectory was exactly what you had brought forward. In many ways, we’re at the stage of genetics and generating human genome information where we could not just make enough out of variants that we would find to make very confident predictions for consequences for the patients who carry those variants.
In too many cases, we were ending up with Variants of Uncertain Significance, where we were just not confident enough that this will cause the disease, or to know if this is the cause of disease that we have in an individual, or what we could actually do to act on those genetic findings. This was, at first for me, a strong motivation to move more into the population genetics space and really start leveraging these enormous amounts of genomes and exomes that are becoming available in large numbers now. On the one hand, I could improve clinical genetic skill sets, and on the other hand, now work in industry on generating therapeutic hypotheses from genetic data. I don’t think I can provide an overview of these many genetic tools that are out there, but I think it’s a super dynamic space. We have almost daily new large-scale resources becoming available that provide the source data that information needs to be queried from, and tools that need to be developed to make sense and generate clinical evidence from these resources. Before I talk too much, maybe I can just pass it on to Tom and Alastair.
ALASTAIR: My apologies for the very dramatic entrance, that was not intentional! I’m not trying to grandstand.
ALASTAIR: My integrated versus external webcam decided to throw a bit of a hissy fit, so my apologies.
NOEMÍ: No worries, Al. Tom, maybe you want to follow up with what Heiko said?
ALASTAIR: Sure! I think we’re at an incredible time, actually, for rare disease diagnosis, and I think we’re at a turning point. Just to give a first example, I was part of the effort with Dr. King’s Born Ready Children’s Hospital to get a world record genetic diagnosis a few years ago. What they’ve been doing is using rapid, whole-genome sequencing in the NICU and PICU to diagnose kids who are in severe distress to get these rapid diagnoses. This work has now spread to many different children’s hospitals, and it’s really on the cusp of going nationwide. From a genetic diagnostic perspective, this is a huge leap forward. These kids who are tremendously ill are getting diagnosed very rapidly, and in time to be able to make a massive impact on their lives. We’re able to treat them in time, to, in some cases, have them live relatively normal lives. We’re seeing this happen right now. Tools where we capture the genetic variants that are associated with disease are really helping this happen, so that when you get these genetic variants, you’re able to do the diagnosis and move it forward. I have a couple more examples, but I’ll start with that.
NOEMÍ: Great, thank you, Tom. Al, do you have anything else to add?
ALASTAIR: No, those were very salient points from my two esteemed colleagues, and Mark. I haven’t heard from him yet, but I’m sure has very erudite things to say at some point. And I agree. Things have moved on a long way because of the advent of the genomic era, the technology that makes so much more available to us now. I’m sure everybody on this call agrees, generating DNA sequence data — pretty much anybody with a test tube and a garage can do these days. Interpreting DNA sequence data is really where the challenge lies. Collectively, the effort to try and bring more understanding on multiple levels, both scientific and clinical, is going to help us do better at the diagnosis piece.
Still, I think, and I’m going to throw the word out there, because I know it will probably set everybody’s teeth on edge, the Variant of Uncertain Significance will forever be until we bring more data to bear the thorn in the side of this process. It’s not always clear within the context of a disease, the relevance of a variant, especially when you start to move into diseases where the clinical context in which you’re looking isn’t so completely clear-cut. Obviously, from my background in Rhythm, we look at obesity. That’s a challenging space in which to kind of try and utilize some of these diagnostic tools, but more grist to the mill, more research. Mark, I just have to say, I do need to make a plug for laborious animal experiments, because we wouldn’t be where we are and I certainly wouldn’t be where Rhythm is without those! Anyway, I just felt like I needed to say that in defense of all of those translational researchers out there.
MARK: Yeah, it took so long. I was very jealous of the pace of publication, so it was a very selfish move on my part.
NOEMÍ: Thank you all. I would now like to focus more on genetic evidence and drug development. A 2018 paper by King et al finds that pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approval. With that in mind, what trends are you seeing with respect to the use of genomic data for drug discovery and development?
TOM: An interesting trend that is coming about by this greater access to diagnostics is which diseases are open for treatment. One of the biggest challenges, of course, when you have a new disease that you’re going after is recruiting for your clinical trials, being able to find those patients. If you have a pipeline that’s able to diagnose and identify a much higher percentage of patients up front, then genetic diseases that are a little bit smaller (that you might not necessarily have thought you could go after) suddenly become realistic to target for companies. I think this change hasn’t happened quite yet, but I think you’re going to see in the next five to ten years that a lot of these relatively smaller indications become commercially viable, and we start getting treatments for way more patients.
ALASTAIR: Hi, sorry, I’m back again. I keep trying to stand everybody up. I agree with what Tom says. I think one of the challenges, though, especially in the rare disease space, is changing the mentality and the behavior of the medical world to start wanting to genetically test because they believe that there is something to be gained from doing it. As we talked about, for some of the most severe and obvious genetic disorders, a lot of that groundwork has been laid. One of the challenges that we have found is trying to move the mentality that says, “there’s no reason to test, there’s enough information, I get back from testing to make things worthwhile to my patients. Ultimately, at the end of the day, we need the call to action that says “test, because there’s a therapy that might actually be able to bring relief.” So I agree, ultimately, this is all good. We have to change mentalities that genetic testing is something that can and should be done.
TOM: Can I build on that a little bit? One of the most amazing things working with these kids with these rare diseases is, there often are treatments available, and those treatments can be very inexpensive. Sometimes, there’s a vitamin or a nutritional change that can really make a big difference. Pulling together these different treatments is a really big deal, because medical centers are not necessarily going to be paying more from diagnosing these kids. What you can do is avoid costs and really improve patient care. To me, the story in support of these medical centers doing that has really built, and you’re going to start seeing that change.
HEIKO: I can maybe add to what has been said. Thinking from a more common disease perspective, we have, of course, a strong impact on the more traditional medical genetics and rare disease field, but I think both the King et al paper that you’re referring to, as well as an early Huang-Nelson et al, and some others have now extended to findings of more common diseases. Looking across the industry, there’s now more and more companies who are really enriching their pipelines, not only for rare diseases and I would say niche indications, but also that are following genetic hypotheses for more common indications, where the thought is that, by targeting individuals genetically through supported genes, there will be a likelihood that one can address larger slices or even entire aspects of common diseases. There are a couple of good examples that are coming out of this now or are in progress stages of the pipeline.
ALASTAIR: Yeah, that’s a really good point. Very often, what happens is that the rare disease seeds the understanding of the contribution from the most severe Mendelian kind of context, that then says, we can expand this because now we understand that, while these genes and variants are contributing to these very rare forms of severe disease, there’s also some association of these things with a broader, expanded indication set that drifts out of rare disease. This then changes the priority for different companies, but still could basically define a population that would disproportionately benefit from a targeted therapeutic approach. It’s moving from causality to contribution in genetics.
HEIKO: Exactly. I guess we’re still coming to the point of what excites us the most, but maybe I can really take this on. It’s really this common disease and rare disease aspect that are coming together. Hopefully at some point in time, we won’t have a classification of “pathogenic” vs. “benign” or something in between, but we can really make quantitative estimates: how strongly is a genetic variant contributing to a disease risk, and ideally, also disease progression or response to therapies? That’s really where I feel we are heading towards as a field; the most exciting period to be in genetics at this time.
MARK: One thing I was going to add, just very quickly to build on what Heiko and Al were talking about, was the power of genetics to resolve diagnostic heterogeneity within highly overlapping clinical findings. In particular, at the top of mind for me is neurodegenerative disease, which has a lot of overlapping phenomenology, and psychiatric disease. I think that’s sort of the last frontier of clinical medicine, but we stand to gain a lot more understanding of the nature of these diseases and how they’re caused by studying them, which will immediately contribute to how we then understand and treat them.
TOM: Yeah, I think we’re going to find that a lot of these common diseases are perhaps a large number of rare diseases put together. The success rate for a lot of these rare trials is reasonably high compared to some of the common diseases. I came from the neuroscience background, and our success rate was quite low. You’re right about neurodegeneration, but being able to focus down on a subset where you really know what the mechanism is really helps increase your likelihood of succeeding. As a matter of fact, I think you’re going to see that some of the mechanisms coming out of rare disease — and Al, you were talking about this — are going to be useful in the broader categories as well. My gut tells me that’s actually how we’re going to solve the problem, right? We’re going to treat some rare diseases with effective medications, and those are going to turn out to be really very effective in diseases like Parkinson’s or Alzheimer’s disease.
ALASTAIR: Yeah, and from a scientific standpoint, it seems like everybody on this call is completely aligned, I think there is a slight barrier. I don’t know that the rest of the world in which we operate — the regulatory, the clinical — has yet moved with that. What I find is that there are two worlds, and they’re very binary. There is rare, and there is not rare. The way in which we have to operate for those two things is very distinct, and can be a barrier to development for certain companies. I think there needs to be an understanding that we’re driving a wedge between that black and that white. That binary is creating a lot of gray that is no longer going to necessarily fall neatly into one or the other, but there’s still value in pursuing for the patient. The rest of the world needs to catch up with the things we’re talking about in order to make it practical for companies to then bring these things forward.
NOEMÍ: So I guess my next question would be, what opportunities for improvement do you see?
TOM: I think we’ve been talking about those opportunities. Building from the rare diseases back out to the more general is really good, but also breaking down, as both Mark and Heiko said, the more common diseases to understand which subset that you’re looking at and driving mechanistically appropriate trials is the way to go.
NOEMÍ: Moving on to patient stratification and improving efficacy — There’s this 2016 study by Huang et al that found that development, with 57 of those failing due to inadequate efficacy. In the context of genetics, what factors contribute to efficacy-related failures? Al, maybe you want to start?
ALASTAIR: What factors contribute to efficacy-related failures? The point we’re getting at is that targeted therapies, when you are trying to address an underlying mechanistic contribution to disease, are far more likely to be effective than these orthogonal approaches that try to circumvent the underlying cause, but still to address at the symptomatic level something that is significant to the disease state. To the point that everybody’s making, targeted therapies would seem to be the way forward, as a way of trying to ensure greater efficacy. You can’t guarantee efficacy, and you certainly can’t guarantee response. I think that’s something else that, as we move into that gray area, we’re just discussing the dovetailing from rare into less rare, maybe, not calling it necessarily common. We also have to understand that there’s a lot of other genetics and external and intrinsic factors that play into that disease state that a particular drug just may be running up against and can’t do anything about. That’s going to be on the individual level, rather than on the conceptual disease level. So while we can probably drive more efficacy in a subset of patients for whom the drug is targeted, we can’t guarantee that the response will be uniform in a way that we would see more so in a true rare disease state, like enzyme replacement therapy or true precision therapy.
HEIKO: Also, it sounds great that genetics is doubling the success rates of drug development, but I think we have to keep in mind that the success rate is still fairly low. Maybe 10 of the drugs that are being tested in humans right now will make it to an approved and marketed drug in the end. We do not have a particularly high success rate in the industry that we’re working in, and there are indeed a lot of reasons why. Good hypotheses that look great in pre-clinical models, or even early clinical trials, will fail. I’m working in Biogen. We are very much a neuroscience-focused company, and here, the success rate has been, compared to other indications, particularly low. We are constantly faced with the challenge, how do we best address a target in the brain that is very difficult to access by drugs? How do we modulate intracellular targets, and what are the exact tissues that we need to address? These types of questions can often extend the discovery and development times.
In addition, for a lot of the neurodegenerative targets, we have made the experience that, typically, with a drug treatment and translating our scientific hypotheses into clinics, we are trying to address the diseases in patients that are fairly early in their disease; at stages that probably translate to a lot more rare indications, at stages where a drug can still have have substantial impact on how the disease is going to continue. Here, it’s also a challenge to find the right group of patients at sufficient numbers and power to demonstrate that drugs do actually work. Being very much of an optimist and with a geneticist’s bias, I think this is something where genetics would also be helpful going forward.
ALASTAIR: Yeah, that last point is very salient. A clinical trial is only going to be as successful as the patients and the drug. If you have the wrong patients on the wrong drug, you’re not going to be successful. That’s where the adage that I throw around a lot is true. Something like, on average, each individual carries three thousand single nucleotide variants that could in some way be linked to some sort of phenotype or some sort of disease. It’s distinctly possible that the patient has a phenotype, they have a genotype, but at the end of the day, those two things aren’t related. You’re not necessarily going to be successful in your clinical trial, which comes back all the way to the first thing that we were talking about, which was the importance of bringing to bear the science, so that we’re disambiguating the uncertainty around the genetics that we’re identifying the right patients.
TOM: This reminds me of a study that I was aware of early in my career, where a phase two study in psychiatry was successful. They went to do a follow-up phase two in an expanded population, and it was unsuccessful, but when they did a post-hoc analysis of that expanded population in the original population, the study would have indeed been successful. What this is pointing towards is, if you can very clearly know which is the population that you’re going after, whether or not the mechanism hypothesis makes sense for that population, you can be much more successful. There’s always that drive to go for that larger population, and again, this is where genetics can be helpful. If it can help you determine who those patients are upfront, it’ll let you run the trial much more efficiently. Imagine if you could just immediately identify the 100, 200 patients necessary for that trial and be able to run it very quickly. Then, the approach makes a whole lot more sense. To me, I think genetics is very powerful to help zero in on that population that we think is going to be more amenable for treatment.
NOEMÍ: Thank you, Tom. So then, what do you think should be considered when generating inclusion or exclusion criteria? Heiko, do you want to start?
HEIKO: Well, that’s always a challenge. We personally use Genomenon to help define the patient groups that we feel would respond to drugs in clinical trials and on the market. We are very much aware that genetics research is not being conducted equally across the globe, where we typically run global clinical trials. One of our challenges that we are working through in particular on the development side of our activities is that we have to define the standards of how a variant is called, “pathogenic” or “not pathogenic.” Often, we do need to learn how these variants are linked to the disease; what is the penetrance, what is the impact on a disease progression? One of the biggest challenges is knowing the geography, what carriers of genetic variants we will find, that will then need to be included into our clinical trials. We do need to have some footprint in those areas, where we will identify those patients that will become eligible, or hopefully at some time, also benefit from drugs that will come out of those efforts. It’s still a very much evolving field, I would say, because there’s not clear criteria of how you define the set of markers to stratify a group. From the regulatory interactions, we also see that this a field that is very much evolving still. I’d be happy to hear about what experiences others have had.
MARK: I was just going to follow up by talking about how the FDA and regulatory bodies can help or hurt, and how there can be a productive path forward, given the benefit of having an appropriate, predefined population. The challenge that attends, when you’re submitting for regulatory approval, but then also on the other side of the clinical trial, Heiko, you brought up a very good point about the heterogeneity and diagnostic genetics. Across the world was what you specifically said, but I was thinking, particularly when you’ve got a drug in the market community versus academic or tertiary care facilities, how are we going to be sure that treatment is possible, not just in the upper echelons of medical practice, but all the way down and across the entire community, where most patients are found? Those were the things that came to mind. I’ll put it to the rest of the panelists to see if they can answer to that.
TOM: Well, I think in particular, we have to be a little cautious about subsetting our population with genetics too early, for a couple of reasons. First of all, participants in clinical trials are often a little bit reluctant to be sequenced and have that information contained in the trial. We need a little bit of education on that, because we want it to be more common. Secondly, for a lot of diseases, there are strong diagnostic criteria for that disease, and we don’t want to limit ourselves to a subset of the genetic carriers, we really want to just treat the disease. So if you have a reasonable biomarker, or just reasonable diagnostic criteria, you should go forward with that. Finally, I find that in many cases, our understanding of the disorders actually increases dramatically after we have the treatment on the market. If we have a way to properly treat patients, we’re able to recruit more and more patients and develop an understanding of who has the disease, and our appreciation of which patients are appropriately treated increases, post the clinical trial.
ALASTAIR: Yeah, there are some really great points for everybody on this topic. To Heiko’s point, I couldn’t agree more about standardization, if it were possible for variant classification and interpretation, especially when one is entering into a clinical study. We’ve all seen the papers about six labs all calling the same variant by eight different classifications. It’s a real struggle. I completely agree with standardization, and making sure that, coming back to it, the right patients. If you carry a pathogenic variant, you’re far more likely to respond, you would believe because that’s going to be underlying the contribution to the disease state that your drug is trying to address.
To the point about clinical versus genetic diagnosis, Tom, I agree with that as well. We do not want to be saying genetics is definitive and that it isn’t the medical diagnosis of the disease that really matters. In a lot of cases, the genetics is simply confirmatory of what it is that the clinician has decided. In other cases, unfortunately, there are situations where you need the genetics to diagnose the disease, but I agree with you. From a regulatory path, you would not want for the patient community to box yourself in, to say that genetics has to be the definitive diagnostic criteria. I agree with all of these points.
HEIKO: Maybe I can jump in here, because I disagree with myself. On the one hand, I’m really early clinical scientist, and on the other hand, also engaged in development. Of course, there’s the perspective of how we build our drugs into what sort of public populations. From the preclinical side, I’m really excited about the prospective that we can build mutation-specific drugs. We can really develop now certain modalities towards the distinct findings in very small numbers of patients. We have examples for enervon therapies, and if we have modalities that we know in principle work, and we have figured out the mechanism for how a particular variant contributes to a disease, I feel that’s the type of activities we should fully execute on. This is what may help individual patients most immediately.
This is, of course, totally not scalable. When I talk with commercial colleagues, I think about it from a clinical development perspective. Currently, we cannot just set 100 different trials with 100 different therapeutics in 100 different patients that all have different mutations. We do need to define distinct disease entities. Here, I feel that for each indication that we are pursuing, there is some infighting of different groups that have different perspectives, and some would lean on the niche indication where we have the highest efficacy. If we would go after what others would rather lean on, say, let’s try and treat as many as we can and demonstrate efficacy in a large enough population, we’re taking the risk that efficacy signals may not be so strong. So really, on the one hand, the science drives us closer to the variant-specific therapies, while the way that we operationalize drug discovery and development (and the scale at which we can reach patients) drives us more to the broader patient spectrum.
ALASTAIR: Just along those lines, there was a paper published recently highlighting that the response of patients to a drug could end up becoming a diagnostic criterion within ACMG. The proof of the response of a patient who has indistinct or unclarified genetics could actually end up being something that informs, which I think was really interesting. It’s a bit circular. Do they have the disease to get the drug? Then you try the drug that confirms they have the disease, but at the same time, it’s that iterative learning, using all the tools we have at our disposal. Heiko’s talking about it at the level of pre-clinical, but the clinical could also be meaningful in the future.
TOM: I agree completely. Often, the treatment itself is a fabulous diagnostic, but it’s one that we’re reluctant to use, but for severe disorders, if you can demonstrate a massive change with the treatment, it is pretty great.
HEIKO: The challenge there, though, is learning about that. You need those physicians to publish their work. There needs to be a forum for capturing that sort of data, and then internalizing it into the broader infrastructure. It was a very interesting paper, it was in the ACMG journal. They actually did it with, I think, three or four different diseases, and taught individuals with boost variants whose genetics didn’t raise them to the level of eligibility, but the response to drugs indicated these variants probably are actually the cause.
TOM: I have certainly spoken to physicians that occasionally use that approach in clinical practice, but it’s not routine.
NOEMÍ: So how do you think, then, companion diagnostics affect clinical decision making and target selection? Tom, do you want to start?
TOM: Well, in rare disease, we focus more on disease diagnostics, to be honest. I can speak to how the presence of a diagnostic affects the decisions, and that’s simply to make it much more straightforward, to prioritize the diseases and prioritize participation in trials for who’s got the disease. Most of the time, we don’t do companion diagnostics, specifically, so I’ll hand that over to Al or Heiko.
ALASTAIR: Yeah, companion diagnostics in the rare disease space are few and far between. To Tom’s point, generally what you’re doing is providing diagnostics for the disease, and the drug will follow. I think CDx has often become more relevant in cases, in the oncology space in particular, where there’s got to be a lot of risk/benefit calculation before certain patients go on to what can be very toxic drugs, for good reason. You’ve got to make sure you’ve got the right person on the right drug. I think companion diagnostics can limit utility, especially at the class three sort of level (there are various classes of diagnostics), they would certainly limit utility and uptake in a rare disease world.
HEIKO: I come back to my earlier comment about standardization, which is not so common everywhere. For a lot of the therapeutics that we are trying to develop, we often have to find the means to identify those sub-cohorts among a larger indication spectrum that we feel will meet the criteria for being included into our trials, and also respond the strongest to the treatments that we are providing. There’s also a few templates yet for how companion diagnostics based on genetics should be done. We have oncology, of course, as a great example. We’ve had several years to learn about how, for somatic variants, we can come up with such tools, and then on the other end in the rare disease space, that is then characterized through mutations in distinct genes. But in this in-between, where we have a more common indication, we are slicing and trying to identify those sub-cohorts that are defined primarily through genetics and not primarily through clinical or biomarker signals. That’s still an area where I feel there’s a lot of change coming. We need some improvements.
TOM: Building on that a bit: looking at a different side of it, the reimbursement side, I’m aware of a couple of diseases where the genetic component of the disease is not complete. It might not even be massive, but payers are now, in some cases, requiring genetic confirmation of a risk factor in order to go forward with paying for the treatment. I think this a little bit of a dangerous situation, because a number of diseases have a number of factors that lead to their progression. You want to be able to provide that genetic support for the treatment, but at the same time, we don’t know the whole genetics. A lot of the genetics and promoter regions, etc., are really unclear. Limiting by reimbursement, I think, is a challenge, and there’s something we’re going to have to discuss as a community.
NOEMÍ: How can genetics be used to de-risk clinical trial programs, then? Al, do you want to start?
ALASTAIR: Yeah, I think we’ve touched on that a lot through some of the other conversations. On the level of trying to identify targeted therapies, there needs to be clarity on the significance and the relevance of an identified variant to an individual’s disease state, and thus, their likelihood of responding. Equally, we really don’t want to limit ourselves so much. You can never really balance response and patient number. The more you drive towards ensuring response, the more your patient numbers go down, and the more patients that you add in, the more uncertainty you have that there’s going to be. It’s a bit of a perfectly confounded problem, until science brings all knowledge to bear and completely clarifies the entire space for a given disease and a given mechanism.
MARK: I wonder if I can expound on or add to that question a little bit, in light of some of the things that we’ve talked about — in particular, Al, you talked about this circular process. You have the disease, get the drug, see a response, confirm the disease. Then, we also talked about the specter of the dreaded VUS and how big and looming it is. We all have been touching on the need for standardization, so I wonder if there is a way to relax the ACMG criteria in certain circumstances, where you have a VUS net for all the world in the context of the patient and the circumstances surrounding that diagnostic. You think that it is the cause of the variant, but it’s nevertheless classified as a VUS. Have the lines blurred. It is not such a Manichaean “pathogenic” or “likely path”, and we can do stuff, versus with VUS, we can’t do anything. Is there a way that, without causing damage to the trial of the patient, particularly when we’re not talking about very cytotoxic or toxic therapies, can we treat the VUSs differently, depending on the clinical context of the disease in the patient, etc.?
ALASTAIR: I think what you’re getting at is this context-dependent logic. That’s the problem with ACMG. It’s not designed for any one particular disease state. It’s there as a tool for a clinician. I think you’re right that we need to evolve its specificity, to understand a variant now isn’t just about diagnosing a disease. Ultimately, it could be a barrier to someone getting a drug. I mean, it’s a postcode lottery. Where did you send your information? Where did your physician stand for your genetic report? Get drug, don’t get drug? So I agree, context is really important.
MARK: I was just going to say really quickly, there’s a tension between the idea that there’s a standard framework that’s applicable across the board, versus the need to be context-aware and be certain that you’re applying these rules in the most appropriate way for the disease. We’re seeing modifications that are disease-specific to ACMG, which is great, but the more we do that, the more we move away from the standardized capability. So it’s not yet resolved. It’s a tension, and hopefully, it’s a creative tension; we’re going to get maximal benefit for patients as a result.
HEIKO: I still want to make a comment on the safety question that you had. I think what we have not really discussed yet is biotropy, that typically, we learn more and more that individual genetic variants are not linked to only one disease, but to an entire spectrum of diseases and of different phenotypes. We are already routinely using phenotype-wide association studies, or an approach that allows us to link a genetic signal, not only to the disease that we consider as an indication, but really, to a spectrum of phenotypes, where we have now in biobanks collected some several thousands of other disease endpoints and immediate endpoints that allow us to characterize and determine the relevance of a variant for the indication, but also, in the context of many other diseases.
In some instances, you do find risk-associated variants pointing to a protective effect on an entirely different indication, and vice versa, which does give us some level of information I think that we did not have a couple of years back in drug discovery. Based on such human data, that may point to eventual safety signals that may come up during a drug development program that, like with these types of data and genetics, we will be able to predict earlier than before.
ALASTAIR: What that speaks to, in my mind, the more data we can share between companies now, we’ve got a very centric view of what we’re looking at our genes for. I don’t know what it means to Heiko or to Tom, to see the same genetics in their context, so I think that’s something else that could expedite this process. I don’t think that the four of us are going to sort this out on this call, and I’m certainly not sending Alexion on everything that Rhythm’s generated, but somehow, we need a forum that’s de-identified. It’s a very difficult thing to do, when you talk about corporate organizations, but I think that would be also powerful for moving some of this.
HEIKO: We probably won’t have time to talk about everything, but data sharing is a very big thing. We do need to have as transparent information as possible for the variance data that we are dealing with, that we are trying to integrate in the planning of therapeutics development. The more data that is locked up in individual diagnostic labs and not accessible because they don’t make publishing a priority, the more challenging it will be for us to make the right decisions based on limited genetic insights.
TOM: Heiko, I really liked what you said around the phenotypic space around a variant, because it kind of circles around. You can use the genetics to really focus so that you can look at the phenotypes of the patients, and then connect that with the burgeoning electronic medical record work that’s being done right now, natural language processing, etc., really deepening our understanding of what patients look like. Then you connect it to specific variants, and that also then helps with diagnostics in the beginning, because they’re often some relatively obscure phenotypes that actually can be very important for getting patients diagnosed before their disease progresses too far. I really like how that works together.
NOEMÍ: So I see we have some questions coming through! What are people’s opinions on how testing will be made more universally accessible and available?
ALASTAIR: Well, first and foremost is cost and people being willing to pay for it, whether that be an insurance company or a commercial organization that’s trying to support community building around a disease. To something Tom said earlier, there also needs to be a greater acceptance by the public that genetic testing has value, that it’s a protected thing, that it’s not just data being shared willy-nilly all over the place. Equally so, I think with a lot of what we’re talking about right now, physicians have to learn what they can do with the results that they get. The absence of them being able to say something clearly on what it is that they learn is only going to put the public off further about the fact that there’s any value in doing this. I think the whole thing is very, very circular.
HEIKO: One point that you made is public perception of genetics, which in the past has been very much black and white. It was considered that genetics or having a genetic test will be deterministic of if there will be a disease or not. I think we’re learning more and more from these larger and larger populations that we genetically screen that this just not the case. It’s essentially a diagnostic tool, as others are as well, with the caveat that it may relate to our other family members or others with similar variants. Overall, I think it’s both training of medical professionals as well as perception in the public of what we can do and what we cannot do with the data as it gets away.
ALASTAIR: Yeah, and ultimately, a call to action at the end of doing it is very important for a position. Mark is an MD, so he can probably speak to this more intelligently than I can, but we certainly see that there’s a bit of a split between physicians, who feel like diagnosis in and of itself has value, and then others, who feel like the diagnosis with nothing that follows afterwards, I don’t know what to do for my patient. So that’s something that follows the diagnosis is always going to be galvanizing, to justify the original purpose.
MARK: From my experience on the wards and in the lab as a molecular pathologist, on the wards, we were coached as medical students to ask, why are you ordering the test if it’s not going to change anything about what you’re doing? As medical students, we ordered a CBC every morning, sometimes twice a day, on every patient, and it was a teaching moment from the clinician. In the lab, though, the idea that molecular testing was useful for diagnostics, particularly in rare disease, it’s difficult to undersell that from a patient perspective. The clarity, the lack of a brooding mystery for the patients and their family members, there’s real material value in getting a diagnosis even if it’s not concrete, or if it’s just a direction that you can turn your attention to. Part is prognostic import as we’re increasingly able to make therapeutic decisions.
It’s-context dependent, and it also depends on the maturity of the clinical field in receiving that genetic data. If that clinical discipline has received genetic data even in a siloed disease area in that discipline, they’re much more accepting of that whole modality, versus if it’s a whole discipline that is brand new to genetics, especially as there are older physicians who may not have been trained very recently. There’s some inertia that we need to change to get this adoption, because the technology is moving faster than, typically, a conservative medical clinical discipline would. That’s the benefit, I think, of genetic testing. Those are the headwinds that we face, where sometimes it’ll be a fresh scale, and other times it’ll be blast force challenges, delimiting rates of progress.
ALASTAIR: It’s basically whether we’re listening to the patient or the physician for these things.
MARK: Good question. I feel, particularly in rare disease in patients, having that ground swell of understanding, and getting away from a standard paternalistic model of delivery of genetic medicine, as long as the patient is very well educated, giving the patient advocacy groups and the patients at large some of that power will really effectuate change. I’m hearing from the forefront people on the ground, that’s actually happening now, and leading to positive contributions. The caveat there, and a concern that I had when I was being trained, is direct-to-consumer genetics and the limit of what information you can extract. I have a 50 percent increased risk of getting lung cancer if I smoke. That’s not really actionable, and I worry a bit that if that’s seeding the consumer or patient psyche, there might be a trepidation about or a misunderstanding about what genetics can and can’t do because of that context that was said.
ALASTAIR: On the other hand, it is those kinds of commercial, mass-market genetic ops that are actually done.
TOM: Mark, your point about the value not just being in the treatment is spot on. Especially in the NICU/PICU population, avoiding unnecessary procedures can be absolutely huge. It can spare the patients a lot of really unnecessary suffering, and also give the parents a lot of closure and a lot of understanding about what’s going on. I think the value is actually very, very broad, and there’s been some recent articles written about that I think are well worth reading. It’s not just about the treatment itself.
MARK: We can’t forget the patient in any of this. The patients and their families, that should be the nucleus of our thinking.
TOM: Getting back to the question of how to drive this part, obviously the lower price is a big one, but I also think finding those populations where the value is so clear — again, with the NICU/PICU population, I personally think that perhaps all of that population should be sequenced to help drive the diagnostic odyssey much more quickly. Sequencing those selected populations, where the value to the patient, the value to the hospital, the value to society is really unquestioned, I think is really helpful. Some of the tools that we’re creating around being able to search through EMRs to identify high-risk populations that may have a genetically driven disorder and sequencing those populations makes the story much clearer, especially for areas where we have the treatments available. You’re sequencing the patient in order to get them treated, and potentially, in some cases, cured.
NOEMÍ: Thank you, Mark, Heiko, Tom, and Al. We’re running out of time, so I’m now going to ask you to leave the stage. We’ll be moving to the networking session, where people will be able to ask you their questions directly by also sharing their audio and video, and continue with the discussion.
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